Passivity analysis of memristive neural networks with probabilistic time-varying delays

Abstract

The passivity problem has been further researched for a class of memristive neural networks with probabilistic time-varying delays in this paper. Based on an effective Lyapunov functional and the Wirtinger-type inequality, sufficient conditions are presented to guarantee the passive performance of the memristive models. By establishing a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delays are considered, which were transformed into one with deterministic time-varying delay and stochastic parameters. Moreover, the range of the delays as well as the probability distribution of its variation are all taken into consideration, thus, the results derived in this paper are more reasonable. Finally, the advantages of the proposed techniques are demonstrated by two numerical examples.